Computed tomography (CT) systems and methods are widely used, particularly for medical imaging and diagnosis. CT systems generally create images of one or more sectional slices through a subject's body. A radiation source, such as an X-ray source, irradiates the body from one side. At least one detector on the opposite side of the body receives radiation transmitted through the body. The attenuation of the radiation that has passed through the body is measured by processing electrical signals received from the detector.
A CT sinogram indicates attenuation through the body as a function of position along a detector array and as a function of the projection angle between the X-ray source and the detector array for various projection measurements. In a sinogram, the spatial dimensions refer to the position along the array of X-ray detectors. The time/angle dimension refers to the projection angle of X-rays, which changes as a function of time during a CT scan. The attenuation resulting from a portion of the imaged object (e.g., a vertebra) will trace out a sine wave around the vertical axis. Those portions farther from the axis of rotation correspond to sine waves with larger amplitudes, and the phases of the sine waves correspond to the angular positions of objects around the rotation axis. Performing an inverse Radon transform—or any other image reconstruction method—reconstructs an image from the projection data represented by the sinogram.
In spectral CT, X-rays having various energies traverse a patient, are then detected using an energy-resolving detector, and reconstructed images are generated from the projection data representing the detected X-ray intensities/attenuation. For example, the respective reconstructed images can correspond to the energy bins of the energy-resolving detectors.
Alternatively, the energy-resolved projection data can be decomposed into material components corresponding to high-Z atoms and low-Z atoms. The reconstructed images can then be generated for the material-component sinograms. Often, the two material components can be a bone component and a water component, wherein the water component includes tissues and fluids primarily composed of water (e.g. blood and soft tissue).
The spectral signature of the respective materials is used to determine corresponding material projection lengths for each ray. The projection lengths represent an amount of each material component that the ray passed through on the path from the X-ray source to the X-ray detector, wherein a predefined magnitude and spectral shape is used for the X-ray absorption coefficient. Thus, the absorption represented in the projection data or image data can be transformed into material components.
Material decomposition can be achieved using various types of CT scanner configurations capable of determining the spectral differences in the X-ray attenuation, including: using energy integrating detectors in combination with an X-ray source that can selectively generate different X-ray spectra, or using a broad bandwidth X-ray source in combination with a detector that selectively detects different X-ray energy bands. For example, the photon-counting detectors differentiate between the X-rays having different energies by resolving detected X-rays into energy bins and counting the number of X-rays in each of the bins along each detector element of the detector array.
Because different materials (i.e., materials having high-Z atoms and low-Z atoms, respectively) exhibit different spectral attenuation signatures for X-rays, spectral-CT projection data can be decomposed into material components using a material-decomposition method. Material decomposition can be performed in either the sinogram domain or the image domain. Each domain has its respective advantages and drawbacks for material decomposition.
On the one hand, sinogram domain material decomposition advantageously represents beam-hardening effects accurately, but it is poorly situated to utilize a priori information regarding the imaged object, which is expressed in the image domain. On the other hand, image-domain material decomposition can advantageously use a priori information, e.g., smoothness and volume constraints, but, disadvantageously, image-domain material decomposition can be less accurate because it does not account for various physical effects (e.g., beam hardening and X-ray scatter). Current methods of material decomposition and CT image reconstruction do not simultaneously overcome the respective drawbacks of material decomposition performed in both the sinogram and image domains.